Learn advanced techniques like hyper parameter tuning, deep learning in a step by step manner with examples.
About This Video
Dive into the advanced algorithms such as hyper-parameter tuning and deep learning, and putting your models into production
Practical, solid, real-world examples that will help you get acquainted with the various stages of machine learning using the R language
Explore important machine learning concepts such as neural network, hyper parameter, unsupervised learning
Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more.
In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them.
After that, you’ll see how to tune hyper-parameters using a data set of sonar data and you’ll get to know their properties. Next, you’ll understand unsupervised learning with an example of clustering politicians, where you’ll explore new patterns, understand unsupervised learning, and visualize and cluster the data.
Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, we’ll offer some suggestions for those who wish to practice the concepts further.